An Efficient Classification Method of Uncertain Data with Sampling

Current research on the classification for uncertain data mainly focuses on the structural changes of the classification algorithms. Existing methods have achieved encouraging results; however, they do not take an effective trade-off between accuracy and running time, and they do not have good portability. This paper proposed a new framework to solve the classification problem of uncertain data from data processing point. The proposed algorithm represents the distribution of raw data by a sampling method, which means that the uncertain data are converted into determined data. The proposed framework is suitable for all classifiers, and then, XGBoost is adopted as a specific classifier in this paper. The experimental results show that the proposed method is an effective way of handling the classification problem for uncertain data.